Transaction Categorization Engine - Powered by Gen AI
Meet large language model based AI that powers your financial insights, categorizes transactions in real-time, and gives your customers a simplistic view of money movement
Leverage Swift technologies AI technology for precise transaction categorization, enhancing financial insights and data enrichment at scale.
Accelerate Your Digital Transformation Journey with Swift technologies Transaction Categorization Engine. Swift technologies Transaction Categorization Engine, fueled by generative AI, revolutionizes financial transaction analysis at scale. Its precision knows no bounds, effortlessly handling diverse types of sources, currencies, languages, and formats in real-time. Bid farewell to inaccuracies and non-scalability, as this cutting-edge solution enriches data, empowering platforms like Personal Financial Management (PFM) and Offer Recommendation systems.
Seamlessly integrated with Swift technologies other solutions like PFM, Carbon Footprint Calculator and Real-Time Experience Engine, it forges an unparalleled ecosystem. At the forefront of finance’s digital revolution, it stands tall, showcasing remarkable adaptability across diverse core banking platforms.
Why Choose Swift technologies Transaction Categorization Engine?
Real-time Classification
Our engine uses real-time streaming platforms to enable real-time classification of transactions and supports batch mode categorization as well.
Comprehensive Categorization
Apart from deriving the type of categories, it also derives the merchant’s name, purpose (category) and location of the transaction.
Embedded Generative AI
The use of large language models (LLM) widens our categorization coverage and solves cold start problems across multiple geographies, thus enabling faster adoption.
Category Adaptive to Your Needs
You can easily customize and refine the category and its granularities according to your needs and preferences.
Reversal and Refund Transaction Linkage
Linking reversal and refund with the original transactions based on transaction pattern simplifies tracking of money movement.
Contextual Recommendations
A better understanding of the context and patterns in users’ transactions helps recommendation systems to generate personalized recommendations for financial products, services, or offers.
Key Metrics
5000+ Transactions per Second(Load Tested)
50+ Categories & Sub-Categories
1.5 Million+ Merchant Master Database (Powered by Generative AI)
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Frequently Asked Questions
What is Swift technologies Transaction Categorization Engine?
Swift technologies Transaction Categorization Engine is a generative-AI powered solution that classifies and enriches financial transactions in real time (or batch). It extracts category, merchant name, purpose and location, links refunds/reversals to original payments, and provides data used for PFM, offer recommendations, carbon calculations, spend analytics and other downstream services.
How does this product differ from traditional rule-based categorization?
Unlike rule-based systems, the engine uses large language models (GPT-3.5 Turbo, LLaMA 2 and transformer-based NLP) to interpret free-text descriptions, handle multiple languages, currencies and formats, and address cold-start problems across geographies. This increases coverage, reduces manual rules maintenance, and improves classification for ambiguous or new merchants.
Can the engine run on our cloud or on-premise environment?
Yes. The engine is deployable to AWS, GCP, Azure or on-premise environments and available via a subscription model. It leverages cloud best practices for security and scalability while also supporting on-premise deployments when required.
How accurate and scalable is the engine?
The solution is designed for enterprise scale and has been load tested at 5,000+ transactions per second. Accuracy benefits from LLM enrichment and a 1.5M+ merchant master database. It also supports real-time streaming (Kafka, KSQL) and batch processing to meet different throughput and latency needs.
What integrations and outputs does the engine support?
The engine integrates with streaming platforms (Kafka/KSQL), databases and downstream services such as PFM, offer recommendation engines, carbon footprint calculators and analytics platforms. Outputs include enriched transaction records (category, merchant, location, flags for recurring/irregular payments) suitable for API or event-driven consumption.